Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Brain Tumor Segmentation BRATS-2013 ModelGenesis Dice Score 0.9258 # 2
Lung Nodule Segmentation LIDC-IDRI ModelGenesis IoU 77.62 # 1
Dice 75.86 # 1
Liver Segmentation LiTS2017 ModelGenesis IoU 79.52 # 3
Dice 91.13 # 3
Lung Nodule Detection LUNA2016 FPRED ModelGenesis AUC 98.20 # 2
Pulmonary Embolism Detection PE-CAD FPRED ModelGenesis AUC 88.04 # 1

Methods used in the Paper


METHOD TYPE
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